Asta-Advances in Statistical Analysis最新文献

This paper compares the ability of several econometric and machine learning methods to nowcast GDP in (pseudo) real-time. The analysis takes the example of Dutch GDP over the period 1992Q1–2018Q4 using a broad data set of monthly indicators. It discusses the forecast accuracy but also analyzes the use of information from the large data set of macroeconomic and financial predictors. We find that, on average, the random forest provides the most accurate forecast and nowcasts, whilst the dynamic factor model provides the most accurate backcasts.

This research proposes a method to test and estimate change points in the covariance structure of high-dimensional multivariate series data. Our method uses the trace of the beta matrix, known as Pillai’s statistics, to test the change in covariance matrix at each time point. We study the asymptotic normality of Pillai’s statistics for testing the equality of two covariance matrices when both sample size and dimension increase at the same rate. We test the existence of a single change point in a given time period using Cauchy combination test, the test using an weighted sum of Cauchy transformed p-values, and estimate the change point as the point whose statistic is the greatest. To test and estimate multiple change points, we use the idea of the wild binary segmentation and repeatedly apply the procedure for a single change point to each segmented period until no significant change point exists. We numerically provide the size and power of our method. We finally apply our procedure to finding abnormal behavior in the investment of a private equity fund.

In this paper, we propose a Bayesian quantile regression (QR) approach to jointly model multivariate ordinal data. Firstly, a multivariate latent variable model is used to link the multivariate ordinal data and latent continuous responses and the multivariate asymmetric Laplace (MAL) distribution is employed to construct the joint QR-based working likelihood for the considered model. Secondly, adaptive-(L_{1/2}) penalization priors of regression parameters are incorporated into the working likelihood to implement high-dimensional Bayesian joint QR inference. Markov Chain Monte Carlo (MCMC) algorithm is utilized to derive the fully conditional posterior distributions of all parameters. Thirdly, Bayesian joint relatively QR estimation approach is recommended to result in more efficient estimation results. Finally, Monte Carlo simulation studies and a real instance analysis of multirater agreement data are presented to illustrate the performance of the proposed Bayesian joint relatively QR approach.

This paper focuses on the general linear measurement error model, in which some or all predictors are measured with error, while others are measured precisely. We propose a semi-parametric estimator that works under general mechanisms of measurement error, including differential and non-differential errors. Other popular methods, such as the corrected score and conditional score methods, only work for non-differential measurement error models, but our estimator works in all scenarios. We develop our estimator by considering a family of objective functions that depend on an unspecified weight function. Using statistical error analysis and perturbation theory, we derive the optimal weight function under the small-sigma regime. The resulting estimator is statistically optimal in all senses. Even though we develop it under the small-sigma regime, we also establish its consistency and asymptotic normality under the large sample regime. Finally, we conduct a series of numerical experiments to confirm that the proposed estimator outperforms other existing methods.

Benford’s law is a particular discrete probability distribution that is often satisfied by the significant digits of a dataset. The nonconformity with Benford’s law suggests the possible presence of data manipulation. This paper introduces two novel generalized versions of Benford’s law that are less restrictive than the original Benford’s law—hence, leading to more probable conformity of a given dataset. Such generalizations are grounded on the existing mathematical relations between Benford’s law probability distribution elements. Moreover, one of them leads to a set of probability distributions that is a proper subset of that of the other one. We show that the considered versions of Benford’s law have a geometric representation on the three-dimensional Euclidean space. Through suitable optimization models, we show that all the probability distributions satisfying the more restrictive generalization exhibit at least acceptable conformity with Benford’s law, according to the most popular distance measures. We also present some examples to highlight the practical usefulness of the introduced devices.

It is often of primary interest to analyze and forecast the levels of a continuous phenomenon as a categorical variable. In this paper, we propose a new spatio-temporal model to deal with this problem in a binary setting, with an interesting application related to the COVID-19 pandemic, a phenomena that depends on both spatial proximity and temporal auto-correlation. Our model is defined through a hierarchical structure for the latent variable, which corresponds to the probit-link function. The mean of the latent variable in the proposed model is designed to capture the trend and the seasonal pattern as well as the lagged effects of relevant regressors. The covariance structure of the model is defined as an additive combination of a zero-mean spatio-temporally correlated process and a white noise process. The parameters associated with the space-time process enable us to analyze the effect of proximity of two points with respect to space or time and its influence on the overall process. For estimation and prediction, we adopt a complete Bayesian framework along with suitable prior specifications and utilize the concepts of Gibbs sampling. Using the county-level data from the state of New York, we show that the proposed methodology provides superior performance than benchmark techniques. We also use our model to devise a novel mechanism for predictive clustering which can be leveraged to develop localized policies.

Considerable research has been devoted to understanding the popularity effect on the art market dynamics, meaning that artworks by popular artists tend to have high prices. The hedonic pricing model has employed artists’ reputation attributes, such as survey results, to understand the popularity effect, but the reputation attributes are constant and not properly defined at the point of artwork sales. Moreover, the artist’s ability has been measured via random effect in the hedonic model, which fails to reflect ability changes. To remedy these problems, we present a method to define the popularity measure using the artwork sales dataset without relying on the artist’s reputation attributes. Also, we propose a novel pricing model to appropriately infer the time-dependent artist’s abilities using the presented popularity measure. An inference algorithm is presented using the EM algorithm and Gibbs sampling to estimate model parameters and artist abilities. We use the Artnet dataset to investigate the size of the rich-get-richer effect and the variables affecting artwork prices in real-world art market dynamics. We further conduct inferences about artists’ abilities under the popularity effect and examine how ability changes over time for various artists with remarkable interpretations.